用外部知识提升大模型推理准确率，减少幻觉。
LLM Inference Enhanced by External Knowledge: A Survey
- 按知识类型分类，分结构化与非结构化数据
- 重点分析表格和知识图谱的融合方法
- 适合关注可信推理与模型可解释性的研究者
大语言模型在自然语言推理方面取得进展，但其参数化记忆有限且易产生幻觉，难以满足精准、上下文依赖的推理需求。为克服这些局限，越来越多研究尝试引入外部知识增强大模型。本文系统梳理了外部知识增强大模型的策略，首先将外部知识分为非结构化与结构化两类；随后聚焦结构化知识，分别对表格和知识图谱建立分类体系，详述其与大模型的集成范式，并回顾代表性方法。通过对比分析，揭示了可解释性、可扩展性与性能之间的权衡，为构建可信且泛化能力强的知识增强型大模型提供参考。
原文摘要 · Abstract (English)
Recent advancements in large language models (LLMs) have enhanced natural-language reasoning. However, their limited parametric memory and susceptibility to hallucination present persistent challenges for tasks requiring accurate, context-based inference. To overcome these limitations, an increasing number of studies have proposed leveraging external knowledge to enhance LLMs. This study offers a systematic exploration of strategies for using external knowledge to enhance LLMs, beginning with a taxonomy that categorizes external knowledge into unstructured and structured data. We then focus on structured knowledge, presenting distinct taxonomies for tables and knowledge graphs (KGs), detailing their integration paradigms with LLMs, and reviewing representative methods. Our comparative analysis further highlights the trade-offs among interpretability, scalability, and performance, providing insights for developing trustworthy and generalizable knowledge-enhanced LLMs.
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